This is becausea>t a takes a large amount of iterations to increase from negative to0.26 Consequently,withalargestepsize,wcanmovefarawayfromw beforea>t a becomesnonnegative. For problems with multiple global optima, our analysis can still be applied if the35 following condition holds: there exists one global optimum such that the PD condition holds globally with respect36 tothis optimum.

Reinforcement learning (RL) [1] studies the problem of learning in sequential decision-making problems where the dynamics of the environment is unknown, but can be learnt by performing actions andobserving their outcome inanonline fashion. Asample-efficient RLagent must trade off the explorationneeded to collect information about the environment, and theexploitation of the experience gathered so far to gain as much reward as possible.

In this work, we apply these ideas to sign language recognition.

The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary theory of online learning provides algorithms for estimation under adaptively

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